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cimport hypergraph
cimport kbest as kb
cdef class Hypergraph:
cdef hypergraph.Hypergraph* hg
cdef MT19937* rng
def __dealloc__(self):
del self.hg
if self.rng != NULL:
del self.rng
def viterbi(self):
cdef vector[WordID] trans
hypergraph.ViterbiESentence(self.hg[0], &trans)
cdef str sentence = GetString(trans).c_str()
return sentence.decode('utf8')
def viterbi_tree(self):
cdef str tree = hypergraph.ViterbiETree(self.hg[0]).c_str()
return tree.decode('utf8')
def viterbi_source_tree(self):
cdef str tree = hypergraph.ViterbiFTree(self.hg[0]).c_str()
return tree.decode('utf8')
def viterbi_features(self):
cdef SparseVector fmap = SparseVector()
fmap.vector = new FastSparseVector[weight_t](hypergraph.ViterbiFeatures(self.hg[0]))
return fmap
def kbest(self, size):
cdef kb.KBestDerivations[vector[WordID], kb.ESentenceTraversal]* derivations = new kb.KBestDerivations[vector[WordID], kb.ESentenceTraversal](self.hg[0], size)
cdef kb.KBestDerivations[vector[WordID], kb.ESentenceTraversal].Derivation* derivation
cdef str sentence
cdef unsigned k
for k in range(size):
derivation = derivations.LazyKthBest(self.hg.nodes_.size() - 1, k)
if not derivation: break
sentence = GetString(derivation._yield).c_str()
yield sentence.decode('utf8')
del derivations
def kbest_tree(self, size):
cdef kb.KBestDerivations[vector[WordID], kb.ETreeTraversal]* derivations = new kb.KBestDerivations[vector[WordID], kb.ETreeTraversal](self.hg[0], size)
cdef kb.KBestDerivations[vector[WordID], kb.ETreeTraversal].Derivation* derivation
cdef str tree
cdef unsigned k
for k in range(size):
derivation = derivations.LazyKthBest(self.hg.nodes_.size() - 1, k)
if not derivation: break
tree = GetString(derivation._yield).c_str()
yield tree.decode('utf8')
del derivations
def sample(self, unsigned n):
cdef vector[hypergraph.Hypothesis]* hypos = new vector[hypergraph.Hypothesis]()
if self.rng == NULL:
self.rng = new MT19937()
hypergraph.sample_hypotheses(self.hg[0], n, self.rng, hypos)
cdef str sentence
cdef unsigned k
for k in range(hypos.size()):
sentence = GetString(hypos[0][k].words).c_str()
yield sentence.decode('utf8')
del hypos
# TODO richer k-best/sample output (feature vectors, trees?)
def intersect(self, Lattice lat):
return hypergraph.Intersect(lat.lattice[0], self.hg)
def prune(self, beam_alpha=0, density=0, **kwargs):
cdef hypergraph.EdgeMask* preserve_mask = NULL
if 'csplit_preserve_full_word' in kwargs:
preserve_mask = new hypergraph.EdgeMask(self.hg.edges_.size())
preserve_mask[0][hypergraph.GetFullWordEdgeIndex(self.hg[0])] = True
self.hg.PruneInsideOutside(beam_alpha, density, preserve_mask, False, 1, False)
def lattice(self): # TODO direct hg -> lattice conversion in cdec
cdef str plf = hypergraph.AsPLF(self.hg[0], True).c_str()
return Lattice(eval(plf))
def reweight(self, weights):
if isinstance(weights, SparseVector):
self.hg.Reweight((<SparseVector> weights).vector[0])
elif isinstance(weights, DenseVector):
self.hg.Reweight((<DenseVector> weights).vector[0])
else:
raise ValueError('cannot reweight hypergraph with %s' % type(weights))
# TODO get feature expectations, get partition function ("inside" score)
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